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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">109</journal-id>
      <journal-id journal-id-type="index">urn:lsid:arphahub.com:pub:3dc5f44e-8666-58db-bc76-a455210e8891</journal-id>
      <journal-title-group>
        <journal-title xml:lang="en">JUCS - Journal of Universal Computer Science</journal-title>
        <abbrev-journal-title xml:lang="en">jucs</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">0948-695X</issn>
      <issn pub-type="epub">0948-6968</issn>
      <publisher>
        <publisher-name>Journal of Universal Computer Science</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.3217/jucs-012-09-1332</article-id>
      <article-id pub-id-type="publisher-id">28680</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group subj-group-type="scientific_subject">
          <subject>G.3 - PROBABILITY AND STATISTICS</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Parameter Estimation of the Cauchy Distribution in Information Theory Approach</article-title>
      </title-group>
      <contrib-group content-type="authors">
        <contrib contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Nagy</surname>
            <given-names>Ferenc</given-names>
          </name>
          <email xlink:type="simple">matnf@gold.uni-miskolc.hu</email>
          <xref ref-type="aff" rid="A1">1</xref>
        </contrib>
      </contrib-group>
      <aff id="A1">
        <label>1</label>
        <addr-line content-type="verbatim">University of Miskolc, , Hungary</addr-line>
        <institution>University of Miskolc</institution>
        <country>Hungary</country>
      </aff>
      <author-notes>
        <fn fn-type="corresp">
          <p>Corresponding author: Ferenc Nagy (<email xlink:type="simple">matnf@gold.uni-miskolc.hu</email>).</p>
        </fn>
        <fn fn-type="edited-by">
          <p>Academic editor: </p>
        </fn>
      </author-notes>
      <pub-date pub-type="collection">
        <year>2006</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>28</day>
        <month>09</month>
        <year>2006</year>
      </pub-date>
      <volume>12</volume>
      <issue>9</issue>
      <fpage>1332</fpage>
      <lpage>1344</lpage>
      <uri content-type="arpha" xlink:href="http://openbiodiv.net/D7EF52B9-55A1-5D8F-9784-FA6B79937113">D7EF52B9-55A1-5D8F-9784-FA6B79937113</uri>
      <uri content-type="zenodo_dep_id" xlink:href="https://zenodo.org/record/6999688">6999688</uri>
      <permissions>
        <copyright-statement>Ferenc Nagy</copyright-statement>
        <license license-type="creative-commons-attribution" xlink:href="" xlink:type="simple">
          <license-p>This article is freely available under the J.UCS Open Content License.</license-p>
        </license>
      </permissions>
      <abstract>
        <label>Abstract</label>
        <p>As we know the Cauchy distribution plays an important role in Probability Theory and Statistics. In this paper, we investigate the estimation of the location and the scale parameter. Both the one-dimensional problem and the multidimensional problem are studied for large sample. In the one-dimensional case, we give two algorithms for the estimation. The first one is an iterative method for which we prove the convergence and we show that the rate of convergence is geometric. The second algorithm provides an exact solution to the problem. In the multidimensional case, we give an algorithm analogous to the one-dimensional case. Computer experiments show that the rate of convergence is similar to the one-dimensional iterative algorithm.</p>
      </abstract>
    </article-meta>
  </front>
</article>
